Key questions of proteomics. Bioinformatics 2. Proteomics. Foundation of proteomics. What proteins are there? Protein digestion
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1 s s Key questions of proteomics What proteins are there? Bioinformatics 2 Lecture X roteomics How much is there of each of the proteins? - Absolute quantitation - Stoichiometry What (modification/splice) state are the proteins in? Which proteins interact with each other or with other molecules (DNA, RNA)? Juri Rappsilber Wellcome Trust Centre for Cell Biology, UoE How does all of the above change with time/stimulation/mutation of a key protein/? Foundation of proteomics Mass spectrometry Algorithms DNA sequencing What proteins are there? rotein identification is achieved by roteolysis of the proteins into peptides Mass spectrometric detection of the peptides (shortcut to protein identification: peptide mass fingerprinting) Mass spectrometric fragmentation of the peptides Database search to identify the peptides rotein digestion eptide mass fingerprinting Isolated protein 1. DTT 2. Iodacetamid 3. Trypsin Digestion K/R MALDI MS Reduction of cysteines by DTT Alkylation of cysteins by IAA Digestion of the protein by trypsin (cleaves after lysine and arginine) Database Query (compare with list of in-silico digests)
2 eptide Fragmentation (Low-Energy Collision induced fragmentation) MS of a eptide Mixture eptides fragment preferentially between amino acids The chemical bond that cleaves depends on the fragmentation method. Low-Energy Collision Induced Dissociation (CID) is most common. Leads to b and y ions Electron Transfer Dissociation (ETD) is up and coming. Leads to c and z ions MS/MS of a eptide (low collision energy) MS/MS of a eptide (high collision energy) E E V V LC-MS interface HV (1600 V) solvent split HLC waste For the analysis of complex mixtures peptides are separated by liquid chromatography that is on-line coupled to a mass spectrometer. => Big datasets (20,000 spectra in 2h analysis, 1,000,000 for an entire experiment possible.) 200 nl/min MS/MS Column (75 μm)/spray tip (8 μm) Many programs available for this matching of fragmentation spectra with peptide sequences from databases (Mascot, Sequest, OMSSA, XTandem!) Each program has its own score. None of the scores is truly statistical. Results for the same dataset vary (overlap between any two ca %). 2
3 How to find the rate of incorrect assignments => confidence? Number of spectra Database iloc.e Target Database search score Targets and decoys v score Targets and s v /H.sapiens target-decoy DB Target FR calculation methods False positive rate = count Target count eptide count Tb Db Locally (within a window around a given score) or cumulative (everything above a given score) Mascot score (score - threshold) Test the impact of FR calculation Two methods for counting the false positives ossibly correct iloc.e cumulative 5% FR Local 5% FR Definitely wrong namuh Target The addition of the human sequences allows us to check if our decoy based approach correctly models our incorrectly identified target peptides. 3
4 eptide counts for cumulative and local methods Non-statistical component of peptide-spectra matches eptides accepted Cumulative and local methods E.g.: Observed fragments do not scatter randomly among the calculated fragments. Number of peptides accepted d ht et cumul local Method used More peptides identified by cumulative method, but also more false peptides included. SVM approach Collect long list of features characterizing the peptide-spectrum match (this includes the score but also other parameters) Use decoy matches as false positives Train the SVM with each dataset new What does the peptide based analysis mean for identifying proteins? Gives significant improvement (20-400%) over search program alone or alternative procedures. Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods Nov;4(11): Epub 2007 Oct 21. Sequence space in the cell Scientific approach Single gene (part of the genome) Organism urification based on a specific function/property Labor Intron Exon Gene expression peptides sequenced by mass spectrometry roteins derived form this gene locus (part of the proteome) Identification Computer rotein sequence database alternative transcripts/translations sequencing error modification of amino acids mispredicted ORF proteolytic processing EST database including alternative transcripts and sequencing errors What does it mean to identify a protein in proteomics? Rappsilber J, Mann M. Trends Biochem Sci Feb;27(2):74-8. Genomic sequence database including SNs and sequencing errors 4
5 Scientific approach Scientific approach urification based on a specific function/property Labor urification based on a specific function/property Labor peptides sequenced by mass spectrometry peptides sequenced by mass spectrometry Identification Computer Identification Computer rotein sequence database rotein sequence database sequencing error sequencing error mispredicted ORF mispredicted ORF EST database EST database including alternative transcripts and sequencing errors including alternative transcripts and sequencing errors Genomic sequence database Genomic sequence database including SNs and sequencing errors including SNs and sequencing errors Modified peptides Include modification as possibility in the database search For informatics the same problem as peptide identification Quantitation in MS Absolute quantitation possible by using a labelled peptide as reference standard. Differential analysis possible by labelling on sample and not labelling the other. Both can then be mixed and analyzed together. Steen H, Mann M. The ABC's (and XYZ's) of peptide sequencing. Nat Rev Mol Cell Biol Sep;5(9): Review. Quantitation in MS In vivo labeling with SILAC Intensity mass difference Stable isotopes D 13 C Cell cultures grown in stable isotope containing media State A State B Combine and digest with trypsin m/z Quantitation by MS 5
6 Analysis of proteins from stressed cells Stoichiometry 300 No/little change in protein abundance upon stress All peptides of a protein are stoichiometric but not observed with identical intensity. Stress induced change in protein abundance Intensity, counts Intensity in mass spectrum not direct consequence of abundance but influenced by many molecule-specific factors => Apple-orange problem m/z Approximation possible by summing up the mass spectrometric evidence gathered for a protein and normalizing this by the expected volume of evidence Example: number of observed peptides / number of observable peptides rotein-protein interactions Can be analyzed using same tools as for protein identification (mass spectrometry and database searching). Need to cross-link proteins to maintain their proximity also after proteolysis. VCLLINKLLR GSTKDVK Normal peptide identification Normal peptide identification Cross-linked I MS Mass match I MS Mass match (n 2 +n)/2 times peptides 6 x > 6 x II MS/MS Fragment match II MS/MS Fragment match 100 -> 5.4 x 10 8 usually low quality spectra more theoretical fragments III Computes THE SCORE III Computes THE SCORE Maiolica A, Cittaro D, Borsotti D, Sennels L, Ciferri C, Tarricone C, Musacchio A, Rappsilber J. Structural analysis of multiprotein complexes by cross-linking, mass spectrometry, and database searching. Mol Cell roteomics Dec;6(12): Epub 2007 Oct 5. 6
7 RB2(228) to RB2(246) m/z (-0.2ppm) 4+ Confidence: high C-α- C-α distance: 33.1Å Chen ZA, Jawhari A, Fischer L, Buchen C, Tahir S, Kamenski T, Rasmussen M, Lariviere L, Bukowski-Wills JC, Nilges M, Cramer, Rappsilber J. Chen ZA, Jawhari A, Fischer L, Buchen C, Tahir S, Kamenski T, Rasmussen M, Lariviere L, Bukowski-Wills JC, Nilges M, Cramer, Rappsilber J. TFIIF Dimerization domain ol II core Chen ZA, Jawhari A, Fischer L, Buchen C, Tahir S, Kamenski T, Rasmussen M, Lariviere L, Bukowski-Wills JC, Nilges M, Cramer, Rappsilber J. Chen ZA, Jawhari A, Fischer L, Buchen C, Tahir S, Kamenski T, Rasmussen M, Lariviere L, Bukowski-Wills JC, Nilges M, Cramer, Rappsilber J. 7
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